CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

Abstract

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods.

Accepted in CIKM 2020

References in corpus (9)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• KGAT: Knowledge Graph Attention Network for Recommendation
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
• DKN: Deep Knowledge-Aware Network for News Recommendation
• Generate Natural Language Explanations for Recommendation
• Explainable Entity-based Recommendations with Knowledge Graphs
• Fairness-Aware Explainable Recommendation over Knowledge Graphs
• Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

Cited by in corpus (14)

• Towards Long-term Fairness in Recommendation
• Explainable Fairness in Recommendation
• Generate Natural Language Explanations for Recommendation
• Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
• DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
• Counterfactual Collaborative Reasoning
• Variation Control and Evaluation for Generative SlateRecommendations
• Personalized Transformer for Explainable Recommendation
• Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
• Faithfully Explainable Recommendation via Neural Logic Reasoning
• Explainable Recommendation with Simulated Human Feedback
• Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails
• Edge: Enriching Knowledge Graph Embeddings with External Text
• Autonomous Business System via Neuro-symbolic AI